If you need help getting data into STATA or doing interpreting values in stata forex operations, see the earlier STATA handout. In the following statistical model, I regress ‘Depend1’ on three independent variables.
Depend1 is a composite variable that measures perceptions of success in federal advisory committees. Do we know for certain that there is something going on? STATA is very nice to you. In some regressions, the intercept would have a lot of meaning. Here it does not, and I wouldn’t spend too much time writing about it in the paper. I’m much more interested in the other three coefficients.
How do I begin to think about them? There are two important concepts here. One is magnitude, and the other is significance. If t is very, very large, then we can use the normal distribution, and the t-statistic is significant if it’s above 1. This table summaries everything from the STATA readout table that we want to know in the paper. After you are done presenting your data, discuss your data. What do the variables mean, are the results significant, etc.
Tell us which theories they support, and what everything means. Note that when the openmeet variable is included, the coefficient on ‘express’ falls nearly to zero and becomes insignificant. In other words, controlling for open meetings, opportunities for expression have no effect. Does the model suffers from ommitted variables or not? In this case, Stata does state the null hypothesis.
I prefer to call it style. Adding interaction terms to a regression model can greatly expand understanding of the relationships among the variables in the model and allows more hypotheses to be tested. 1 if the plant is in full sun. One possibility is that in full sun plants with more bacteria in the soil tend to be taller, whereas in partial sun plants with more bacteria in the soil are shorter. Another possibility is that plants with more bacteria in the soil tend to be taller in both full and partial sun, but that the relationship is much more dramatic in full than in partial sun. The presence of a significant interaction indicates that the effect of one predictor variable on the response variable is different at different values of the other predictor variable. It is tested by adding a term to the model in which the two predictor variables are multiplied.